Thermodynamic Stability Landscape of Halide Double Perovskites via High‐Throughput Computing and Machine Learning. (17th January 2019)

• Record Type: Journal Article
• Authors: Li, Zhenzhu; Xu, Qichen; Sun, Qingde; Hou, Zhufeng; Yin, Wan‐Jian
• Is Part Of: Advanced functional materials. Volume 29:Number 9(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-01-17
• Subjects: halide double perovskite -- high‐throughput -- machine learning -- stability; Materials -- Periodicals; Chemical vapor deposition -- Periodicals; 620.11
• DOI: 10.1002/adfm.201807280
• Languages: English
• ISSNs: 1616-301X

Abstract

Abstract: Formability and stability issues are of core importance and difficulty in current research and applications of perovskites. Nevertheless, over the past century, determination of the formability and stability of perovskites has relied on semiempirical models derived from physics intuition, such as the commonly used Goldschmidt tolerance factor, t . Here, through high‐throughput density functional theory (DFT) calculations, a database containing the decomposition energies, considered to be closely related to the thermodynamic stability of 354 halide perovskite candidates, is established. To map the underlying relationship between the structure and chemistry features and the decomposition energies, a well‐functioned machine learning (ML) model is trained over this theory‐based database and further validated by experimental observations of perovskite formability ( F 1 score, 95.9%) of 246 A2 B(I)B(III)X6 compounds that are not present in the training database; the model performs a lot better than empirical descriptors such as tolerance factor t ( F 1 score, 77.5%). This work demonstrates that the experimental engineering of stable perovskites by ML could solely rely on training data derived from high‐throughput DFT computing, which is much more economical and efficient than experimental attempts at materials synthesis. Abstract : High‐throughput density‐functional theory (DFT) calculations and a machine learning (ML) approach are combined to provide comprehensive comparisons between the empirical rule and ML on perovskite stability and to demonstrate that ML trained solely on high‐throughput DFT computing had great predictive power for experimental formability of perovskites.